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To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against ground impact caused by road debris is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE model. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise direction, magnitude, and location of the force applied from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian process regression (GPR), to perform inverse analysis of the direction, magnitude, and location of vehicle-road contact forces during rough road conditions.

Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.

The purpose of this study is to propose braking characteristics that are easy for drivers to handle in a system in which braking and driving operations are performed by hand. Genetic algorithm optimization of braking characteristics showed that the best deceleration tracking was achieved by an FG diagram with a logarithmic function shape. In contrast, the slope of the optimal FG diagram tended to decrease as the driver's proportional gain increased.

Teammate Advanced Drive is a driving support system with state-of-the-art automated driving technology that has been developed for customers’ safe and secure driving on highways based on the Toyota’s Mobility Teammate Concept. This SAE Level 2 (L2) system assists overtaking, lane changes, and branching to the destination, in addition to providing hands-free lane centering and car following. The automated driving technology includes self-localization onto a High Definition Map, multi-modal sensing to cover 360 degrees of the surrounding environment using fusion of LiDARs, cameras, and radars, and a redundant architecture to realize fail-safe operation when a malfunction or system limitation occurs. High-performance computing is provided to implement deep learning for predicting and responding to various situations that may be encountered while driving.

Collisions between opened doors and approaching vehicles such as bicycles are common occurrences in urban areas around the world. For example, in Chicago, 20% of all bicycle accidents involve collisions with doors, which occur over 300 times a year. In addition, there are concerns about a further rise in accidents due to the recent increase in home delivery services and bicycle commuting during the COVID-19 pandemic. Some advanced driver assistance systems (ADAS) that are designed to help prevent this type of accident have already been introduced. These systems detect approaching vehicles with sensors and alert the person opening the door via LED lights or a buzzer when the door is opened. The occupant must understand the meaning of the alert and stop opening the door quickly to prevent an accident. However, if the occupant is an elderly person or a child, it is difficult to stop opening the door quickly.

This paper presents a design method for continuous fiber composites in three-dimensional space with locally varying orientation distribution and their fabrication method. The design method is formulated based on topology optimization by augmented tensor field design variables. The fabrication method is based on Tailored Fiber Placement technology, whereby a CNC embroidery machine prepares the preform. The fiber path is generated from an optimized orientation distribution field. The preform is formed with vacuum-assisted resin transfer molding. The fabricated prototype weighs 120 g, a 70% weight reduction, achieving 3.5× mass-specific stiffness improvement.

Due to new CO2 regulations and increasing demand for improved fuel economy, reducing aerodynamic drag has become more critical. Aerodynamic drag at the rear of the vehicle accounts for approximately 40% of overall aerodynamic drag due to low base pressure in the wake region. Many studies have focused on the wake region structure and shown that drag reduction modifications such as boattailing the rear end and sharpening the rear edges of the vehicle are effective. Despite optimization using such modifications, recent improvements in the aerodynamic drag coefficient (Cd) seem to have plateaued. One reason for this is the fact that vehicle design is oriented toward style and practicality. Hence, maintaining flexibility of design is crucial to the development of further drag reduction modifications. The purpose of this study was to devise a modification to reduce rear drag without imposing additional design restrictions on the upper body.

Woven Core, Inc. has developed Teammate Advanced Drive, a driving support system with state-of-the-art automated driving technology based on the Mobility Teammate Concept by Toyota Motor Corporation. Teammate Advanced Drive enables intelligent Ramp to Ramp hands-off driving on highways. The system features a self-localization estimation system that uses an HD-Map (High Definition Map) and high-level redundancy across sensors, computing, actuators, power supplies, and data communication. The system also includes digital data uploading and downloading capabilities wirelessly OTA (Over the Air) in order to provide customers the latest map updates as well as new software features and upgraded performance. A number of characteristically unique sensors have been combined to monitor the entire perimeter of the vehicle with high reliability.

In recent years, GPFs (Gasoline particulate filters) have been installed in gasoline engines to comply with stricter environmental regulations in China and Europe. In particular, coated-GPFs having a catalytic purification function are required to have high conversion performances, high filter efficiencies in the sense of a high collection efficiency, and low pressure loss. It is not easy to design a filter that satisfies all these parameters. Experimental studies are being conducted, but it is costly to study in trial productions. In this technical paper, a GPF design optimization method will be proposed that combines multi-scale simulation, surrogate models by machine learning, and an optimization algorithm. By using this method, a GPF design that minimizes pressure loss while providing high conversion performance and particle collection rates that satisfy current regulations can be created.

Routing quality always dominates the top 20% of in vehicle- navigation customer complaints. In vehicle navigation routing engines do not customize results based on customer behavior. For example, some users prefer the quickest route while some prefer direct routes. This is because in vehicle navigation systems are traditionally embedded systems. Toyota announced that new model vehicles in JP, CN, US will be connected with routing function switching from the embedded device to the cloud in which there are plenty of probe data uploaded from the vehicles. Probe data makes it possible to analyze user preferences and customize routing profile for users. This paper describes a method to analyze the user preferences from the probe data uploaded to the cloud. The method includes data collection, the analysis model of route scoring and user profiling. Furthermore, the evaluation of the model will be introduced at the end of the paper.

When the air conditioning (A/C) is turned on, the intake air to the HVAC is cooled at the evaporator. This is not only used for cooling the air temperature but also to dehumidify. Therefore, for a typical automatic climate control system, A/C will automatically operate even in winter (cold ambient temperature conditions) in order to prevent the windows from fogging despite its effect on fuel economy. In some applications, a humidity sensor is installed on top of the windshield and when the probability of fogging is low the A/C operation is disabled automatically to prevent unnecessary compressor operation which can increase fuel consumption. However, humidity sensor is not widely adopted as it requires some space to be installed and the cost is relatively expensive compared with other HVAC equipped sensors. In this study, a system was invented that disables the compressor operation when the fogging probability is low without using the conventional humidity sensor.

Given a forecast of speed and load demands during a trip, a hybrid powertrain power-split Trajectory Optimization Problem (TOP) can be solved to optimize fuel consumption. This can be done on desktop to set performance benchmarks; however, it has been believed that the TOP could not be solved in real-time and is not a realizable controller. As such, several approximations of the TOP have been made in the interest of obtaining a real-time near-optimal controller, for example, Equivalent Consumption Minimization Strategies (ECMS) and their adaptive counterparts. These strategies decide on the power-split by, at each sampled time instant, minimizing a Horizon-0 (without predicting forward in time) composite function of fuel consumption and equivalent battery energy. The fuel economy that results from these strategies is highly sensitive to the calibration of the associated equivalence factor, and furthermore, must be chosen differently for different drive cycles.

In the early stages of vehicle development, it is important for decision makers to understand a feasible constraint region that satisfies all system level requirements. The purpose of this paper is to propose a target cascading method to solve for a feasible design region which satisfies all constraints of the system based on model based simulation. In this method, the feasible design region is explored by using both global optimization methods and active learning techniques. In optimization problems, the inverse problem for understanding feasibility for specific designs is defined and solved. To determine the objective functions of the inverse problem, an index representing the achievement level of constraints from system requirements is introduced. To predict feasible regions in the specific design space, a surrogate model of minimized values of the index is trained by using a kriging model.

This paper presents the thermal management of a hybrid vehicle (HV) using a heat pump system in cold weather. One advantage of an HV is the high efficiency of the vehicle system provided by the coupling and optimal control of an electric motor and an engine. However, in a conventional HV, fuel economy degradation is observed in cold weather because delivering heat to the passenger cabin using the engine results in a reduced efficiency of the vehicle system. In this study, a heat pump, combined with an engine, was used for thermal management to decrease fuel economy degradation. The heat pump is equipped with an electrically driven compressor that pumps ambient heat into a water-cooled condenser. The heat generated by the engine and the heat pump is delivered to the engine and the passenger cabin because the engine needs to warm up quickly to reduce emissions and the cabin needs heat to provide thermal comfort.

In the last decade, extensive efforts have been made to understand the physics of submarining and its consequences in terms of abdominal injuries. For that purpose, 27 Post Mortem Human Subject (PMHS) tests were performed in well controlled conditions on a sled and response corridors were provided to assess the biofidelity of dummies or human body models. All these efforts were based on the 50th percentile male. In parallel, efforts were initiated to transfer the understanding of submarining and the prediction criteria to the THOR dummies. Both the biofidelity targets and the criteria were scaled down from the 50th percentile male to the 5th percentile THOR female. The objective of this project was to run a set of reference PMHS tests in order to check the biofidelity of the THOR F05 in terms of submarining. Three series of tests were performed on nine PMHS, the first one was designed to avoid submarining, the second and third ones were designed to result in submarining.

This article proposes a rubber suspension bushing model considering amplitude dependence as a useful tool at the initial design phase. The purpose of this study is not to express physical phenomena accurately and in detail and to explore the truth academically, but to provide a useful design method for initial design phase. Experiments were carried out to verify several dynamic characteristics of rubber bushings under vibration up to a frequency of 100 Hz, which is an important frequency range when designing ride comfort performance. When dynamic characteristic theory and the geometrical properties of the force-displacement characteristic curve were considered using these dynamic characteristics as assumptions, an equation was derived that is capable of calculating the dynamic stiffness under an arbitrary amplitude by identifying only two general design parameters (dynamic stiffness and loss factor) under a reference amplitude.

Petit et al. 2015 and Lebarbé et al. 2016 reported on two studies where the injury mechanism and threshold of the sacroiliac joint were investigated in two slightly oblique crash test conditions from 18 Post Mortem Human Subjects (PMHS) tests. They concluded that the sacroiliac joint fractures were associated with pubic rami fractures. These latter being reported to occur first in the time history. Therefore it was recommended not to define a criterion specific for the sacroiliac joint. In 2012, injury risk curves were published for the WorldSID dummy by Petitjean et al. For the pelvis, dummy and PMHS paired tests from six configurations were used (n = 55). All of these configurations were pure lateral impacts. In addition, the sacroiliac joint and femur neck loads were not recorded, and the dummy used was the first production version (WorldSID revision 1). Since that time, the WorldSID was updated several times, including changes in the pelvis area.

To resolve two major problems of conventional CFD-based shape optimization technology: (1) dependence of the outcome on the selection of design parameters, and (2) high computational costs, two types of innovative inverse analysis technologies based on a mathematical theory called the Adjoint Method were developed in previous studies for maximizing an arbitrary hydrodynamic performance aspect as the cost function: surface geometry deformation sensitivity analysis to identify the locations to be modified, and topology optimization to generate an optimal shape. Furthermore, these technologies were extended to transient flows by the application of the transient Adjoint Method theory. However, there are many cases around flow path shapes in vehicles where performance with respect to heat or concentration, such as the total amount of heat transfer or the flow rate of a specific gas component, is very important.

For the purpose of coping with the strengthening of NOx exhaust gas control and fuel consumption control, it is indispensable to improve the NOx purification capacity. In view of this, vehicle manufacturers are in the course of developing high performance SCR (Selective Catalytic Reduction) systems [1, 2]. For such SCR systems to be realized, high precision NOx sensors for carrying out urea injection quantity control and SCR degradation diagnosis are absolutely indispensable. Detection of NOx concentration by means of a NOx sensor is generally performed as follows: O2 is discharged by means of an O2 detection electrode; remaining NOx is decomposed by a NOx detection electrode; NOx concentration is then detected as electric current that flows when oxygen ions are conduct through solid electrolyte. In order to detect NOx of ppm-order, it is necessary to detect minute current of nA-order with high accuracy.

In an effort to reduce mass, future automotive bodies will feature lower gage steel or lighter weight materials such as aluminum. An unfortunate side effect of lighter weight bodies is a reduction in sound transmission loss (TL). For barrier based systems, as the total system mass (including the sheet metal, decoupler, and barrier) goes down the transmission loss is reduced. If the reduced surface density from the sheet metal is added to the barrier, however, performance can be restored (though, of course, this eliminates the mass savings). In fact, if all of the saved mass from the sheet metal is added to the barrier, the TL performance may be improved over the original system. This is because the optimum performance for a barrier based system is achieved when the sheet metal and the barrier have equal surface densities. That is not the case for standard steel constructions where the surface density of the sheet metal is higher than the barrier.